Overview
- Understand what SQL and NoSQL databases are.
- Review the highlighted difference between SQL and No SQL databases.
- This is not an exhaustive list. Feel free to add any other differences between SQL and NoSQL in the comments
Introduction
You can't stop learning about databases in data science. In fact, we need to get quite familiar with how to handle databases, how to run queries quickly, etc. as data science professionals. There's no way to avoid it!
There are two things you should know: learn all you can about database administration and then find out how to do it efficiently. Créame, you will go a long way in the domain of data science.
As a data engineer, is obliged to work with all kinds of databases, especially SQL and NoSQL. But nevertheless, most of us already have considerable experience with SQL databases. Where we fail is when we have to transition to NoSQL databases, and it can be a bit intimidating at first, to be honest, the beginning is always the hardest.
Then, to flatten the obstacle for you, We will talk about some key differences between these two types of databases in this article.. This will give you an overview of the two and make it easier for you to start your journey.. Let's start!
Table of Contents
- What are SQL databases?
- What are NoSQL databases?
- Difference between SQL and NoSQL databases
- Schematic design
- Data structure
- Speed
- Climbing
- Use
- Main address, main guide
What are SQL databases?
SQL is a standard query language that helps query relational databases. Therefore, these databases are also often called SQL databases.
The main advantage of databases over normal file storage systems is that it greatly reduces data redundancy., facilitates the exchange of data between multiple users and ensures the security of data that can be of immense importance to an organization.
Each database contains multiple tables, containing data in the form of rows and columns. And each table is related to other tables within the database.
What are NoSQL databases?
NoSQL or Not only SQL appeared on the scene at the end of the decade of 2000. It's about flexible databases, scalable, profitable and no scheme.
They were born from the need to handle large amounts of data that we generate in today's world, that come in different varieties and are generated at an accelerated rate.
Compared to SQL databases, they are of various types: document-based, based on key values, based on wide columns, graphics-based. Each one has its pros and cons.
Now let's dive in and see some of the key differences between SQL and NoSQL databases..
Difference between SQL and NoSQL databases
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Schematic design
SQL databases is it so relational databases that store data in multiple related tables. These tables are relations. Each relationship is organized in rows and columns. Each row is a double and has a record, and each column is a attribute so each record usually has a value. Database tables are related using SQL keys.
The table columns contain a certain type of data. If a record contains data with any other data type, the database will throw an error. What's more, A record must contain the same number of values as the number of columns in the table, or it must explicitly provide a NULL value. The most popular examples of SQL databases are MySQL, PostgreSQL y Oracle.
There is 4 NoSQL database types: document-based, based on key values, wide column-based and chart-based.
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Document-based databases
These databases store data in JSON-like documents. Each document has a key-value format, which means that the data is semi-structured. Even if a value is missing within a document for a key, the database will not throw an error. A popular example is MongoDB.
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Key-value databases
These databases store data in key-value format. Both keys and values can be anything, from strings to complex values. Keys are stored in efficient index structures and can locate values quickly and uniquely. This makes them ideal for applications that require fast data recovery.. Amazon DynamoDB is an example of these databases.
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Extensive column-based databases
This database stores data in records similar to any relational database, but it has the ability to store a large number of dynamic columns. Namely, the number of column values for rows may vary in those databases. Groups columns logically into column families. Cassandra is a popular example.
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Graph-based databases
They use nodes to store data entities like places, products, etc. and edges to store the relationship between them. There is no limit to the number and type of relationships a node can have. Neo4j is an example of these databases.
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Data structure
Determining the structure or schema of the database before adding any data is a prerequisite for SQL databases. This means that this type of database can only store structured data. This makes it very inflexible to handle real world data that is transmitted at a fierce rate.. Updating the schema here would take a lot of time and effort and would need to update a lot of relationships.
NoSQL databases Secondly, they do not have a fixed structure. They can handle any type of data: structured, semi-structured or unstructured. This means that even if the incoming data has a different number of attributes, the database will be able to handle them without any error. This makes NoSQL databases very popular because we can easily change the schema without much interruption.
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Speed
There is no real difference between the two when it comes to speed. Both will work equally well in most scenarios. But nevertheless, you may notice some differences when it comes to handling complex queries and large data sets.
SQL databases require data storage to be in standardized form to avoid data redundancy. Although this reduces the amount of storage required by the database and ensures easy updating of records, may have some effect on the database query. For instance, performing complex queries like joins on a database containing multiple tables can be quite tiring, especially when the data size becomes quite significant. NoSQL databases overcome this disadvantage.
NoSQL databases it doesn't matter if there is data duplication because storage is not an issue with NoSQL databases. Data in NoSQL databases is generally stored in a query-optimized way. This means that you can store data in the same way that you would need it after making a query. This rules out the whole problem of joins and makes the task of querying much quicker.
For instance, SQL databases require you to keep two separate tables for employee information and department information, linking them with a foreign key, maybe the department id.
But nevertheless, in the case of NoSQL databases, como MongoDB, can store the complete information about the employee, including department information, within the same document, although you can do some value nesting if you want.
Note: can still perform joins on NoSQL databases.
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Climbing
SQL databases run on traditional machines. This means they run on a single server. Now, if you exceed the current capacity of your server, would have to use a more powerful CPU, add more RAM, stack storage, etc. This is vertical scale. This can be quite expensive, especially if you have to deal with Big Data (on the order of TB, GB, PB, etc.)
Secondly, NoSQL databases offer horizontal scale. This means that if you run out of capacity, you can simply add a machine to the cluster (a group of machines working together). These machines are usually much cheaper and are known as basic hardware. This ability of NoSQL databases has another important advantage in addition to cheaper capacity building which is data distribution.
NoSQL databases generally run on multiple interconnected machines, what is known as a cluster. Data is distributed among machines within the cluster. Each machine will store a part of the data.
Now you must ask yourself how is this beneficial.
Good, distributing data offers us the ability to replicate data and offer Fault tolerance. Namely, a part of the data can be replicated and stored on multiple machines.
If a machine fails, the data it contains will be present on some other machine in the cluster and can be used without the knowledge of the user, thus offering fault tolerance. Obviously, this is not possible with SQL databases because the storage of all data is on the same machine.
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Main address, main guide
A great benefit of SQL databases is his ability to handle transactional processing. These processes modify the content of a database. The ACID properties of SQL databases govern:
- Atomicidad – Transactions take place one at a time or do not occur at all.
- Consistency – This ensures that the database is not left in the middle of a full state. If an error occurs, makes sure rollback changes occur.
- Isolation – Transactions occur independently. No transaction has access to any other transaction.
- Durability – Changes made to the database through transactions upon completion are committed to the database and updates are not lost.
NoSQL databases Secondly, do not provide ACID properties completely. However, the CAP theorem governs them:
- Consistency – This means that the user they should be able to see the same data no matter which node / machine are connected to the system / cluster. Then, if data has been written to a node, must be replicated on all its replicas.
- Availability – This means that every user request should get a response from the system. Whether the user wants to read or write, the user should get a response even if the operation was unsuccessful.
- Partition tolerance – Partition occurs when a node cannot receive any messages from another node in the system. It could have been due to a network failure, server failure or any other reason. Therefore, partition tolerance will ensure that the system can still work even if there is a partition in the system.
But nevertheless, NoSQL databases have to make a trade-off between consistency and availability when partitioning occurs. This is because, in a real world system, the partition is likely to occur due to a network failure or some other reason. Therefore, when a partition occurs, a NoSQL database has to compromise on consistency or availability. Therefore, a NoSQL distributed database is characterized as CP or AP.
Note: NoSQL databases are not that rigid when it comes to CAP. Most offer options to balance consistency and availability. Therefore, the choice is not always so black and white.
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Use
The ACID property makes SQL databases extremely important in fields where transactions are extremely important. Banking is an example where money transactions must be handled correctly, especially in the case of a failed transfer, whose failure can cost a fortune.
What's more, whether your data will be structured and will not change, no reason to use NoSQL databases. You can always take advantage of the capabilities of your SQL databases and, of course, his stellar knowledge of SQL!
But nevertheless, if you are looking to work with a large volume of data without an established structure, NoSQL databases are the best option. But even NoSQL databases can have a wide-ranging use case depending on the inherent structure and your preference for the properties of the CAP theorem..
While, on the one hand, ElasticSearch stores log data, Cassandra, Secondly, is used by many social media websites. But nevertheless, All of this at the end of the day is helping to manage the volume, the speed and variety of Big Data!
Final notes
In this article, we discuss the main differences between SQL and NoSQL databases. This is by no means an exhaustive list of differences between the two databases. But hopefully, You have a good description of both!
Looking to the future, I recommend you try the SQL for data science course and the following articles on SQL and NoSQL:
